Silicon carbide (SiC) is chemically stable, highly heat-resistant, and resistant to thermal shock. SiC having excellent characteristics in a high temperature and high voltage environment is used in high-power semiconductors, highprecision mechanical devices, optical components, etc. As it is used in various industries, there is a growing demand for processing fine holes or grooves in silicon carbide. In this study, micro holes and grooves were machined on 4HSiC and sintered SiC using electrical discharge machining (EDM). Silicon carbide which has very high hardness can be easily processed by EDM as compared with mechanical processes. As a tool material, a polycrystalline diamond (PCD) which has high wear resistance was used and a micro tool of a diameter of 100 μm was fabricated by wire electrical discharge grinding (WEDG). In the EDM of SiC, the machining characteristics such as surface roughness, discharge gap, and tool wear were investigated.
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Here in, a high-quality automotive camera lens was developed based on an ultra-precision diamond turning core and cyclic olefin polymer (COP) injection molding process. To improve surface roughness and achieve the accuracy of plastic injection molding lens, systematic mold core machining process was developed and demonstrated using the diamond turning machine. The cutting tool path was generated by using NanoCAM 2D, and it was partly revised to prevent interference between the cutting tool and the workpiece. After the initial machining using the generated tool path, the compensation-cutting process was conducted based on the measured surface profile of an initially machined surface. After two times of compensation machining, the fabricated core mold showed a shape error of 100 nm between peak to valley (PV) and Arithmetic mean roughness (Ra) of 3.9 nm. The performance of the fabricated core was evaluated using an injection molding test. Injection molded aspheric plastic lens showed contrasts that were higher than 55% at 0.0 F, 30% at 0.3 F, and 20% at 0.7 F without any moiré phenomenon that meets the specification for automotive vision module with 1MP and 140° field of view.
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This study analyzed acoustic emission (AE) signals generated during ultrasonic machining of SiC cathodes and evaluated classification performances of various machine learning models. AE data were collected in both waveform and hit formats, enabling signal characterization through statistical analysis and frequency domain examination. Various machine learning models, including XGBoost, KNN, Logistic Regression, SVM, and MLP, were applied to classify machining states. Results showed that XGBoost achieved the highest classification accuracy across all sensor positions, particularly at the upper part of the worktable with an accuracy of 98.35%. Additional experiments confirmed the consistency of these findings, highlighting the influence of sensor placement on classification performance. This study demonstrates the feasibility of monitoring AE-based machining state using machine learning and emphasizes the importance of sensor placement and signal analysis in improving classification accuracy. Future research should incorporate defect data and deep learning approaches to further enhance classification performance and process monitoring capabilities.
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